{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:21:04Z","timestamp":1788153664333,"version":"build-2803163510"},"reference-count":62,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100030922","name":"Institut de recherche, Centre universitaire de sant\u00e9 McGill","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100030922","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To improve on a multiparametric technique\u2014known as Fat DESPOT\u2014to map the fat\u2010 and water\u2010specific  (, ), , and proton density fat fraction (PDFF) by upgrading the parameter initialization and introducing explicit model sensitivity to the complex phase of the water and fat signals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We compared three\u2010point Dixon and Graph Cut (GC) approaches to initial guesses for Fat DESPOT in experiments at 3\u2009T in fat emulsion phantoms. Also in the phantom, we compared magnitude\u2010based Fat DESPOT (Fat DESPOT\n                      <jats:sub>\n                        <jats:italic>m<\/jats:italic>\n                      <\/jats:sub>\n                      ) to a magnitude approach modeling the phases of fat and water separately (Fat DESPOT\n                      <jats:sub>\n                        <jats:italic>m\u03d5<\/jats:italic>\n                      <\/jats:sub>\n                      ), and two models for complex data (Fat DESPOT\n                      <jats:sub>\n                        <jats:italic>c<\/jats:italic>\n                      <\/jats:sub>\n                      , Fat DESPOT\n                      <jats:sub>\n                        <jats:italic>c\u03d5<\/jats:italic>\n                      <\/jats:sub>\n                      ). The complex approaches, which performed best, were used in the lower leg and abdomen of healthy human participants.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In phantoms, Fat DESPOT using three\u2010point Dixon and GC returned similar parameter estimates and precision, though the former deviated from the overall trend at 50% nominal fat fraction. The complex models showed the best agreement with reference PDFF (average error 1.2%\u2009\u00b1\u20091.2%) and the lowest combined standard deviation across ROIs, for PDFF, , and  (maximum \u2009=\u20090.13%, 0.19\u2009s\n                      <jats:sup>\u22121<\/jats:sup>\n                      , and 0.009\u2009s\n                      <jats:sup>\u22121<\/jats:sup>\n                      , respectively).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>With more precise  and , more accurate PDFF, and more versatile echo time selection, this work demonstrates the advantages of Fat DESPOT multiparametric mapping with complex fitting and GC\u2010based parameter initialization.<\/jats:p>\n                  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